Fake Currency Detection using CNN Test phase, srs, design phase and source code final deliverable

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Fake Currency Detection using CNN Test phase, srs, design phase and source code final deliverable

Domain/Category Image Processing

Abstract / Introduction

Image processing is the method of performing operations on an image in order to improve it or obtain useful information from it. Image processing has various applications in fields such as medical, defense, industry, remote sensing, pattern recognition, and video processing, among many others.

Due to the advancements in computers and laser printers, the number of fake currency notes are increasing day by day. It is very important to efficiently identify fake notes from actual notes using an automatic procedure. To overcome this problem, we will create an automatic system using CNN to identify fake currency notes. Functional Requirements:

  1. For this project, you have to use only two types of currency notes, i.e., 500Rs and 1000Rs.
  2. Create a dataset of real currency notes, i.e., 500 and 1000.
  3. Create a dataset of fake currency notes, i.e., 500 and 1000.
  4. Create 200 images of each category as a dataset.
  5. Take images in extra light so that their water marks or other security features are visible.
  6. Divide the dataset into a ratio of 80:20 for training and testing, respectively.
  7. Use a pre-trained convolutional neural network known as Alex Net.
  8. After training, the system should be able to differentiate between fake and real currency notes.
  9. A proper interface using MATLAB should be created for all these activities.

10.Use different built-in functions of MATLAB where applicable.

Note: Virtual University of Pakistan will not provide any kind of hardware for this project, student must arrange required hardware by himself/herself.

Tools & Technologies:

Preferred tool and technology: MATLAB (Any latest version of MATLAB)


Name: Noor Rahman


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